ArticleBMC musculoskeletal disorders2026
Automated assessment of coronal lower extremity alignment on long-leg radiographs using a deep-learning model: validation, efficiency gains, and superiority of an edge-based tibial joint-line definition.
Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo develop, validate, and benchmark a fully automated deep learning (DL) system that simultaneously measures 15 coronal lower-limb alignment parameters on standing long-leg radiographs (LLRs) and localizes deformity, and to compare two tibial joint-line definitions for suitability in DL-based measurement.
methodsA retrospective set of 309 anteroposterior standing LLRs was split into training/validation/testing (60/20/20). External generalizability was assessed using 75 independent LLRs from a different scanner and patient cohort. YOLOv5 was used to detect multiple bony landmarks, followed by algorithmic calculation of 15 parameters (e.g., HKAA, mLDFA, mMPTA). Two tibial joint-line definitions were evaluated: Method 1 (line through medial/lateral lowest tibial plateau points) and Method 2 (line through most medial/lateral plateau edges). Accuracy, clinical failure rate (≥ 2° or ≥ 2%), and runtime were compared with expert consensus annotations. Bland-Altman analysis was added to assess measurement bias and clinical agreement.
resultsOn the external dataset, absolute errors for knee phenotype-related parameters ranged from 0.07° to 0.45°. Automated analysis of all 15 parameters took 24.3 ± 0.7 s, reducing time by 89-91% versus manual measurement (p < 0.001). Method 2 produced significantly smaller absolute errors than Method 1 for nearly all parameters (p ≤ 0.005). Clinically significant failure rates were low (0-4.7%) and were significantly lower than an attending physician's for several key metrics on the external set. The distribution of varus/valgus/neutral alignment based on HKAA and extraarticular deformity locations were reported.
conclusionThis DL framework provides fast, comprehensive, and specialist-level coronal alignment assessment on LLRs. An edge-based tibial joint-line definition (Method 2) outperforms a lowest-point definition, improving precision and reliability for DL measurement pipelines, supporting clinically deployable orthopedic imaging AI. Future prospective studies are warranted to validate Method 2 against clinical outcomes including osteoarthritis progression and surgical results.
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